Multiverse implementation of a simulated dataset on the impact of social media usage
Abhraneel Sarma, Northwestern University
2023-03-07
library(dplyr)
library(tidyr)
library(ggplot2)
library(purrr)
library(ordinal)
library(dagitty)
library(broom)
library(gganimate)
library(cowplot)
library(multiverse)knit_as_emar()Abstract
In this paper, we investigate the relationship between social media usage in adolescents and depression, using a multiverse analysis [@Steegen2016]. Our findings suggest that the relationship is inconclusive, as the effect of social media usage regressed on self-reported ratings of depression appears to be possibly both positive and negative, depending on arbitrary choices in the data analysis process.
Introduction
With the growing use of digital technology use, especially by younger, impressionable individuals, its impact on their well-being is of growing concern. Recent work has suggested that there may be an small, negative association between digital technology use and adolescent well-being [@Orben2019]. In this work, we focus specifically on usage of social media and its potential impact on mental well-being. Frequent social media usage can expose adolescents to toxic content, which is abundant on online platforms [@Vogel2021]. For example, Instagram has many posts with ``pro-eating disorder’’ content which it has struggled to moderate [@Chancellor2016, @Chancellor2016].
To better assess the harms of social media usage on adolescents, we conduct a survey with N = 300 participants and investigate the association between social media usage and depression using a multiverse analysis. We report our findings below.
Data
Our survey questionnaire consists of 10 items which includes the following:
- I1: self reported number of hours spent on social media per week
- I2: self reported likert scale rating of social media usage (1 [very low/no usage at all] - 5 [very high usage])
- I3: self reported number of friends/followers on social media across all platforms
- I3: self reported number of posts shared in a week
- self-reported age
- self reported number of hours per week engaged in physical activity or exercise of any kind
- Binary choice indicating whether participant has siblings
- Age of siblings (if any)
- Total parental income (rounded to the nearest $10k)
social_media = readRDS("../../mvis/data/social_media.rds")
head(social_media)## # A tibble: 6 × 11
## .id parent_i…¹ age sibli…² has_s…³ depre…⁴ physi…⁵ I1 I2 I3 I4
## <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 99000 14 12 1 4 4 19 3 116 2
## 2 2 101000 14 14 1 4 2 15 2 93 2
## 3 3 88000 13 13 1 2 4 15 3 52 1
## 4 4 83000 15 13 1 3 4 21 4 74 1
## 5 5 99000 14 14 1 4 4 20 4 108 4
## 6 6 63000 14 12 1 2 7 12 2 63 3
## # … with abbreviated variable names ¹parent_income, ²sibling_age,
## # ³has_siblings, ⁴depression, ⁵physical_activityM = multiverse()Analysis
We plan to use a linear regression model with depression as the dependent variables.
Exclusion criteria
As the data was collected through a survey, we may expect there to be some outliers. We consider four alternatives for outlier_exclusion : - analysing all cases (no exclusion) - cutting off values which are 2.5 SD from the mean - cutting off values which are 3.5 SD from the mean - cutting off values based on Tukey’s fences (first and third quartiles \(\pm\) 1.5 times the interquartile range)
The results in the current analysis reflect
df = social_media
IQR.I1 = quantile(df$I1, probs = 0.75) - quantile(df$I1, probs = 0.25)
IQR.I2 = quantile(df$I2, probs = 0.75) - quantile(df$I2, probs = 0.25)
IQR.I3 = quantile(df$I3, probs = 0.75) - quantile(df$I3, probs = 0.25)
IQR.I4 = quantile(df$I4, probs = 0.75) - quantile(df$I4, probs = 0.25)
df = df %>%
filter(TRUE) %>%
mutate(I1 = scale(I1), I2 = scale(I2), I3 = scale(I3), I4 = scale(I4), )df = social_media
IQR.I1 = quantile(df$I1, probs = 0.75) - quantile(df$I1, probs = 0.25)
IQR.I2 = quantile(df$I2, probs = 0.75) - quantile(df$I2, probs = 0.25)
IQR.I3 = quantile(df$I3, probs = 0.75) - quantile(df$I3, probs = 0.25)
IQR.I4 = quantile(df$I4, probs = 0.75) - quantile(df$I4, probs = 0.25)
df = df %>%
filter((((I1 >= (quantile(I1, probs = 0.25) - 1.5 * IQR.I1)) & (I1 < (quantile(I1,
probs = 0.75) + 1.5 * IQR.I1))) & ((I2 >= (quantile(I2, probs = 0.25) - 1.5 *
IQR.I2)) & (I2 < (quantile(I2, probs = 0.75) + 1.5 * IQR.I2))) & ((I3 >=
(quantile(I3, probs = 0.25) - 1.5 * IQR.I3)) & (I3 < (quantile(I3, probs = 0.75) +
1.5 * IQR.I3))) & ((I4 >= (quantile(I4, probs = 0.25) - 1.5 * IQR.I4)) &
(I4 < (quantile(I4, probs = 0.75) + 1.5 * IQR.I4))))) %>%
mutate(I1 = scale(I1), I2 = scale(I2), I3 = scale(I3), I4 = scale(I4), )df = social_media
IQR.I1 = quantile(df$I1, probs = 0.75) - quantile(df$I1, probs = 0.25)
IQR.I2 = quantile(df$I2, probs = 0.75) - quantile(df$I2, probs = 0.25)
IQR.I3 = quantile(df$I3, probs = 0.75) - quantile(df$I3, probs = 0.25)
IQR.I4 = quantile(df$I4, probs = 0.75) - quantile(df$I4, probs = 0.25)
df = df %>%
filter((((I1 > (mean(I1) - 2.5 * sd(I1))) & (I1 < (mean(I1) + 2.5 * sd(I1)))) &
((I2 > (mean(I2) - 2.5 * sd(I2))) & (I2 < (mean(I2) + 2.5 * sd(I2)))) & ((I3 >
(mean(I3) - 2.5 * sd(I3))) & (I3 < (mean(I3) + 2.5 * sd(I3)))) & ((I4 > (mean(I4) -
2.5 * sd(I4))) & (I4 < (mean(I4) + 2.5 * sd(I4)))))) %>%
mutate(I1 = scale(I1), I2 = scale(I2), I3 = scale(I3), I4 = scale(I4), )df = social_media
IQR.I1 = quantile(df$I1, probs = 0.75) - quantile(df$I1, probs = 0.25)
IQR.I2 = quantile(df$I2, probs = 0.75) - quantile(df$I2, probs = 0.25)
IQR.I3 = quantile(df$I3, probs = 0.75) - quantile(df$I3, probs = 0.25)
IQR.I4 = quantile(df$I4, probs = 0.75) - quantile(df$I4, probs = 0.25)
df = df %>%
filter((((I1 > (mean(I1) - 3.5 * sd(I1))) & (I1 < (mean(I1) + 3.5 * sd(I1)))) &
((I2 > (mean(I2) - 3.5 * sd(I2))) & (I2 < (mean(I2) + 3.5 * sd(I2)))) & ((I3 >
(mean(I3) - 3.5 * sd(I3))) & (I3 < (mean(I3) + 3.5 * sd(I3)))) & ((I4 > (mean(I4) -
3.5 * sd(I4))) & (I4 < (mean(I4) + 3.5 * sd(I4)))))) %>%
mutate(I1 = scale(I1), I2 = scale(I2), I3 = scale(I3), I4 = scale(I4), )df = social_media
IQR.I1 = quantile(df$I1, probs = 0.75) - quantile(df$I1, probs = 0.25)
IQR.I2 = quantile(df$I2, probs = 0.75) - quantile(df$I2, probs = 0.25)
IQR.I3 = quantile(df$I3, probs = 0.75) - quantile(df$I3, probs = 0.25)
IQR.I4 = quantile(df$I4, probs = 0.75) - quantile(df$I4, probs = 0.25)
df = df %>%
filter(branch(
outlier_exclusion,
"no_exclusion" ~ TRUE,
"one-half_interquartile_range" ~ (
((I1 >= (quantile(I1, probs = 0.25) - 1.5 * IQR.I1)) & (I1 < (quantile(I1, probs = 0.75) + 1.5*IQR.I1))) &
((I2 >= (quantile(I2, probs = 0.25) - 1.5 * IQR.I2)) & (I2 < (quantile(I2, probs = 0.75) + 1.5*IQR.I2))) &
((I3 >= (quantile(I3, probs = 0.25) - 1.5 * IQR.I3)) & (I3 < (quantile(I3, probs = 0.75) + 1.5*IQR.I3))) &
((I4 >= (quantile(I4, probs = 0.25) - 1.5 * IQR.I4)) & (I4 < (quantile(I4, probs = 0.75) + 1.5*IQR.I4)))
),
"two-half_sd_from_mean" ~ (
((I1 > (mean(I1) - 2.5 * sd(I1))) & (I1 < (mean(I1) + 2.5 * sd(I1)))) &
((I2 > (mean(I2) - 2.5 * sd(I2))) & (I2 < (mean(I2) + 2.5 * sd(I2)))) &
((I3 > (mean(I3) - 2.5 * sd(I3))) & (I3 < (mean(I3) + 2.5 * sd(I3)))) &
((I4 > (mean(I4) - 2.5 * sd(I4))) & (I4 < (mean(I4) + 2.5 * sd(I4))))
),
"three-half_sd_from_mean" ~ (
((I1 > (mean(I1) - 3.5 * sd(I1))) & (I1 < (mean(I1) + 3.5 * sd(I1)))) &
((I2 > (mean(I2) - 3.5 * sd(I2))) & (I2 < (mean(I2) + 3.5 * sd(I2)))) &
((I3 > (mean(I3) - 3.5 * sd(I3))) & (I3 < (mean(I3) + 3.5 * sd(I3)))) &
((I4 > (mean(I4) - 3.5 * sd(I4))) & (I4 < (mean(I4) + 3.5 * sd(I4))))
)
)) %>%
mutate(
I1 = scale(I1),
I2 = scale(I2),
I3 = scale(I3),
I4 = scale(I4),
)Covariate selection
We consider the inclusion/exclusion of three covariates:
parental_income as a covariate will likely
decrease precision, and thus we do not include this variable in our
regression.
fit = lm(depression ~ social_media_usage + NULL + NULL + NULL, data = df)fit = lm(depression ~ social_media_usage + NULL + NULL + factor(has_siblings), data = df)fit = lm(depression ~ social_media_usage + NULL + physical_activity + NULL, data = df)fit = lm(depression ~ social_media_usage + NULL + physical_activity + factor(has_siblings),
data = df)fit = lm(depression ~ social_media_usage + age + NULL + NULL, data = df)fit = lm(depression ~ social_media_usage + age + NULL + factor(has_siblings), data = df)fit = lm(depression ~ social_media_usage + age + physical_activity + NULL, data = df)fit = lm(depression ~ social_media_usage + age + physical_activity + factor(has_siblings),
data = df)fit = lm(depression ~ social_media_usage + NULL + NULL + NULL, data = df)fit = lm(depression ~ social_media_usage + NULL + NULL + factor(has_siblings), data = df)fit = lm(depression ~ social_media_usage + NULL + physical_activity + NULL, data = df)fit = lm(depression ~ social_media_usage + NULL + physical_activity + factor(has_siblings),
data = df)fit = lm(depression ~ social_media_usage + age + NULL + NULL, data = df)fit = lm(depression ~ social_media_usage + age + NULL + factor(has_siblings), data = df)fit = lm(depression ~ social_media_usage + age + physical_activity + NULL, data = df)fit = lm(depression ~ social_media_usage + age + physical_activity + factor(has_siblings),
data = df)fit = lm(depression ~ social_media_usage + NULL + NULL + NULL, data = df)fit = lm(depression ~ social_media_usage + NULL + NULL + factor(has_siblings), data = df)fit = lm(depression ~ social_media_usage + NULL + physical_activity + NULL, data = df)fit = lm(depression ~ social_media_usage + NULL + physical_activity + factor(has_siblings),
data = df)fit = lm(depression ~ social_media_usage + age + NULL + NULL, data = df)fit = lm(depression ~ social_media_usage + age + NULL + factor(has_siblings), data = df)fit = lm(depression ~ social_media_usage + age + physical_activity + NULL, data = df)fit = lm(depression ~ social_media_usage + age + physical_activity + factor(has_siblings),
data = df)fit = lm(depression ~ social_media_usage + NULL + NULL + NULL, data = df)fit = lm(depression ~ social_media_usage + NULL + NULL + factor(has_siblings), data = df)fit = lm(depression ~ social_media_usage + NULL + physical_activity + NULL, data = df)fit = lm(depression ~ social_media_usage + NULL + physical_activity + factor(has_siblings),
data = df)fit = lm(depression ~ social_media_usage + age + NULL + NULL, data = df)fit = lm(depression ~ social_media_usage + age + NULL + factor(has_siblings), data = df)fit = lm(depression ~ social_media_usage + age + physical_activity + NULL, data = df)fit = lm(depression ~ social_media_usage + age + physical_activity + factor(has_siblings),
data = df)fit = lm(depression ~ social_media_usage + NULL + NULL + NULL, data = df)fit = lm(depression ~ social_media_usage + NULL + NULL + factor(has_siblings), data = df)fit = lm(depression ~ social_media_usage + NULL + physical_activity + NULL, data = df)fit = lm(depression ~ social_media_usage + NULL + physical_activity + factor(has_siblings),
data = df)fit = lm(depression ~ social_media_usage + age + NULL + NULL, data = df)fit = lm(depression ~ social_media_usage + age + NULL + factor(has_siblings), data = df)fit = lm(depression ~ social_media_usage + age + physical_activity + NULL, data = df)fit = lm(depression ~ social_media_usage + age + physical_activity + factor(has_siblings),
data = df)fit = lm(depression ~ social_media_usage + NULL + NULL + NULL, data = df)fit = lm(depression ~ social_media_usage + NULL + NULL + factor(has_siblings), data = df)fit = lm(depression ~ social_media_usage + NULL + physical_activity + NULL, data = df)fit = lm(depression ~ social_media_usage + NULL + physical_activity + factor(has_siblings),
data = df)fit = lm(depression ~ social_media_usage + age + NULL + NULL, data = df)fit = lm(depression ~ social_media_usage + age + NULL + factor(has_siblings), data = df)fit = lm(depression ~ social_media_usage + age + physical_activity + NULL, data = df)fit = lm(depression ~ social_media_usage + age + physical_activity + factor(has_siblings),
data = df)fit = lm(depression ~ social_media_usage + NULL + NULL + NULL, data = df)fit = lm(depression ~ social_media_usage + NULL + NULL + factor(has_siblings), data = df)fit = lm(depression ~ social_media_usage + NULL + physical_activity + NULL, data = df)fit = lm(depression ~ social_media_usage + NULL + physical_activity + factor(has_siblings),
data = df)fit = lm(depression ~ social_media_usage + age + NULL + NULL, data = df)fit = lm(depression ~ social_media_usage + age + NULL + factor(has_siblings), data = df)fit = lm(depression ~ social_media_usage + age + physical_activity + NULL, data = df)fit = lm(depression ~ social_media_usage + age + physical_activity + factor(has_siblings),
data = df)fit = lm(depression ~ social_media_usage + NULL + NULL + NULL, data = df)fit = lm(depression ~ social_media_usage + NULL + NULL + factor(has_siblings), data = df)fit = lm(depression ~ social_media_usage + NULL + physical_activity + NULL, data = df)fit = lm(depression ~ social_media_usage + NULL + physical_activity + factor(has_siblings),
data = df)fit = lm(depression ~ social_media_usage + age + NULL + NULL, data = df)fit = lm(depression ~ social_media_usage + age + NULL + factor(has_siblings), data = df)fit = lm(depression ~ social_media_usage + age + physical_activity + NULL, data = df)fit = lm(depression ~ social_media_usage + age + physical_activity + factor(has_siblings),
data = df)fit = lm(depression ~ social_media_usage + NULL + NULL + NULL, data = df)fit = lm(depression ~ social_media_usage + NULL + NULL + factor(has_siblings), data = df)fit = lm(depression ~ social_media_usage + NULL + physical_activity + NULL, data = df)fit = lm(depression ~ social_media_usage + NULL + physical_activity + factor(has_siblings),
data = df)fit = lm(depression ~ social_media_usage + age + NULL + NULL, data = df)fit = lm(depression ~ social_media_usage + age + NULL + factor(has_siblings), data = df)fit = lm(depression ~ social_media_usage + age + physical_activity + NULL, data = df)fit = lm(depression ~ social_media_usage + age + physical_activity + factor(has_siblings),
data = df)fit = lm(depression ~ social_media_usage + NULL + NULL + NULL, data = df)fit = lm(depression ~ social_media_usage + NULL + NULL + factor(has_siblings), data = df)fit = lm(depression ~ social_media_usage + NULL + physical_activity + NULL, data = df)fit = lm(depression ~ social_media_usage + NULL + physical_activity + factor(has_siblings),
data = df)fit = lm(depression ~ social_media_usage + age + NULL + NULL, data = df)fit = lm(depression ~ social_media_usage + age + NULL + factor(has_siblings), data = df)fit = lm(depression ~ social_media_usage + age + physical_activity + NULL, data = df)fit = lm(depression ~ social_media_usage + age + physical_activity + factor(has_siblings),
data = df)fit = lm(depression ~ social_media_usage + NULL + NULL + NULL, data = df)fit = lm(depression ~ social_media_usage + NULL + NULL + factor(has_siblings), data = df)fit = lm(depression ~ social_media_usage + NULL + physical_activity + NULL, data = df)fit = lm(depression ~ social_media_usage + NULL + physical_activity + factor(has_siblings),
data = df)fit = lm(depression ~ social_media_usage + age + NULL + NULL, data = df)fit = lm(depression ~ social_media_usage + age + NULL + factor(has_siblings), data = df)fit = lm(depression ~ social_media_usage + age + physical_activity + NULL, data = df)fit = lm(depression ~ social_media_usage + age + physical_activity + factor(has_siblings),
data = df)fit = lm(depression ~ social_media_usage + NULL + NULL + NULL, data = df)fit = lm(depression ~ social_media_usage + NULL + NULL + factor(has_siblings), data = df)fit = lm(depression ~ social_media_usage + NULL + physical_activity + NULL, data = df)fit = lm(depression ~ social_media_usage + NULL + physical_activity + factor(has_siblings),
data = df)fit = lm(depression ~ social_media_usage + age + NULL + NULL, data = df)fit = lm(depression ~ social_media_usage + age + NULL + factor(has_siblings), data = df)fit = lm(depression ~ social_media_usage + age + physical_activity + NULL, data = df)fit = lm(depression ~ social_media_usage + age + physical_activity + factor(has_siblings),
data = df)fit = lm(depression ~ social_media_usage + NULL + NULL + NULL, data = df)fit = lm(depression ~ social_media_usage + NULL + NULL + factor(has_siblings), data = df)fit = lm(depression ~ social_media_usage + NULL + physical_activity + NULL, data = df)fit = lm(depression ~ social_media_usage + NULL + physical_activity + factor(has_siblings),
data = df)fit = lm(depression ~ social_media_usage + age + NULL + NULL, data = df)fit = lm(depression ~ social_media_usage + age + NULL + factor(has_siblings), data = df)fit = lm(depression ~ social_media_usage + age + physical_activity + NULL, data = df)fit = lm(depression ~ social_media_usage + age + physical_activity + factor(has_siblings),
data = df)fit = lm(depression ~ social_media_usage + NULL + NULL + NULL, data = df)fit = lm(depression ~ social_media_usage + NULL + NULL + factor(has_siblings), data = df)fit = lm(depression ~ social_media_usage + NULL + physical_activity + NULL, data = df)fit = lm(depression ~ social_media_usage + NULL + physical_activity + factor(has_siblings),
data = df)fit = lm(depression ~ social_media_usage + age + NULL + NULL, data = df)fit = lm(depression ~ social_media_usage + age + NULL + factor(has_siblings), data = df)fit = lm(depression ~ social_media_usage + age + physical_activity + NULL, data = df)fit = lm(depression ~ social_media_usage + age + physical_activity + factor(has_siblings),
data = df)fit = lm(depression ~ social_media_usage + NULL + NULL + NULL, data = df)fit = lm(depression ~ social_media_usage + NULL + NULL + factor(has_siblings), data = df)fit = lm(depression ~ social_media_usage + NULL + physical_activity + NULL, data = df)fit = lm(depression ~ social_media_usage + NULL + physical_activity + factor(has_siblings),
data = df)fit = lm(depression ~ social_media_usage + age + NULL + NULL, data = df)fit = lm(depression ~ social_media_usage + age + NULL + factor(has_siblings), data = df)fit = lm(depression ~ social_media_usage + age + physical_activity + NULL, data = df)fit = lm(depression ~ social_media_usage + age + physical_activity + factor(has_siblings),
data = df)fit = lm(depression ~ social_media_usage + NULL + NULL + NULL, data = df)fit = lm(depression ~ social_media_usage + NULL + NULL + factor(has_siblings), data = df)fit = lm(depression ~ social_media_usage + NULL + physical_activity + NULL, data = df)fit = lm(depression ~ social_media_usage + NULL + physical_activity + factor(has_siblings),
data = df)fit = lm(depression ~ social_media_usage + age + NULL + NULL, data = df)fit = lm(depression ~ social_media_usage + age + NULL + factor(has_siblings), data = df)fit = lm(depression ~ social_media_usage + age + physical_activity + NULL, data = df)fit = lm(depression ~ social_media_usage + age + physical_activity + factor(has_siblings),
data = df)fit = lm(depression ~ social_media_usage + NULL + NULL + NULL, data = df)fit = lm(depression ~ social_media_usage + NULL + NULL + factor(has_siblings), data = df)fit = lm(depression ~ social_media_usage + NULL + physical_activity + NULL, data = df)fit = lm(depression ~ social_media_usage + NULL + physical_activity + factor(has_siblings),
data = df)fit = lm(depression ~ social_media_usage + age + NULL + NULL, data = df)fit = lm(depression ~ social_media_usage + age + NULL + factor(has_siblings), data = df)fit = lm(depression ~ social_media_usage + age + physical_activity + NULL, data = df)fit = lm(depression ~ social_media_usage + age + physical_activity + factor(has_siblings),
data = df)fit = lm(depression ~ social_media_usage + NULL + NULL + NULL, data = df)fit = lm(depression ~ social_media_usage + NULL + NULL + factor(has_siblings), data = df)fit = lm(depression ~ social_media_usage + NULL + physical_activity + NULL, data = df)fit = lm(depression ~ social_media_usage + NULL + physical_activity + factor(has_siblings),
data = df)fit = lm(depression ~ social_media_usage + age + NULL + NULL, data = df)fit = lm(depression ~ social_media_usage + age + NULL + factor(has_siblings), data = df)fit = lm(depression ~ social_media_usage + age + physical_activity + NULL, data = df)fit = lm(depression ~ social_media_usage + age + physical_activity + factor(has_siblings),
data = df)fit = lm(depression ~ social_media_usage + NULL + NULL + NULL, data = df)fit = lm(depression ~ social_media_usage + NULL + NULL + factor(has_siblings), data = df)fit = lm(depression ~ social_media_usage + NULL + physical_activity + NULL, data = df)fit = lm(depression ~ social_media_usage + NULL + physical_activity + factor(has_siblings),
data = df)fit = lm(depression ~ social_media_usage + age + NULL + NULL, data = df)fit = lm(depression ~ social_media_usage + age + NULL + factor(has_siblings), data = df)fit = lm(depression ~ social_media_usage + age + physical_activity + NULL, data = df)fit = lm(depression ~ social_media_usage + age + physical_activity + factor(has_siblings),
data = df)fit = lm(depression ~ social_media_usage + NULL + NULL + NULL, data = df)fit = lm(depression ~ social_media_usage + NULL + NULL + factor(has_siblings), data = df)fit = lm(depression ~ social_media_usage + NULL + physical_activity + NULL, data = df)fit = lm(depression ~ social_media_usage + NULL + physical_activity + factor(has_siblings),
data = df)fit = lm(depression ~ social_media_usage + age + NULL + NULL, data = df)fit = lm(depression ~ social_media_usage + age + NULL + factor(has_siblings), data = df)fit = lm(depression ~ social_media_usage + age + physical_activity + NULL, data = df)fit = lm(depression ~ social_media_usage + age + physical_activity + factor(has_siblings),
data = df)fit = lm(depression ~ social_media_usage + NULL + NULL + NULL, data = df)fit = lm(depression ~ social_media_usage + NULL + NULL + factor(has_siblings), data = df)fit = lm(depression ~ social_media_usage + NULL + physical_activity + NULL, data = df)fit = lm(depression ~ social_media_usage + NULL + physical_activity + factor(has_siblings),
data = df)fit = lm(depression ~ social_media_usage + age + NULL + NULL, data = df)fit = lm(depression ~ social_media_usage + age + NULL + factor(has_siblings), data = df)fit = lm(depression ~ social_media_usage + age + physical_activity + NULL, data = df)fit = lm(depression ~ social_media_usage + age + physical_activity + factor(has_siblings),
data = df)fit = lm(depression ~ social_media_usage + NULL + NULL + NULL, data = df)fit = lm(depression ~ social_media_usage + NULL + NULL + factor(has_siblings), data = df)fit = lm(depression ~ social_media_usage + NULL + physical_activity + NULL, data = df)fit = lm(depression ~ social_media_usage + NULL + physical_activity + factor(has_siblings),
data = df)fit = lm(depression ~ social_media_usage + age + NULL + NULL, data = df)fit = lm(depression ~ social_media_usage + age + NULL + factor(has_siblings), data = df)fit = lm(depression ~ social_media_usage + age + physical_activity + NULL, data = df)fit = lm(depression ~ social_media_usage + age + physical_activity + factor(has_siblings),
data = df)fit = lm(depression ~ social_media_usage + NULL + NULL + NULL, data = df)fit = lm(depression ~ social_media_usage + NULL + NULL + factor(has_siblings), data = df)fit = lm(depression ~ social_media_usage + NULL + physical_activity + NULL, data = df)fit = lm(depression ~ social_media_usage + NULL + physical_activity + factor(has_siblings),
data = df)fit = lm(depression ~ social_media_usage + age + NULL + NULL, data = df)fit = lm(depression ~ social_media_usage + age + NULL + factor(has_siblings), data = df)fit = lm(depression ~ social_media_usage + age + physical_activity + NULL, data = df)fit = lm(depression ~ social_media_usage + age + physical_activity + factor(has_siblings),
data = df)fit = lm(depression ~ social_media_usage + NULL + NULL + NULL, data = df)fit = lm(depression ~ social_media_usage + NULL + NULL + factor(has_siblings), data = df)fit = lm(depression ~ social_media_usage + NULL + physical_activity + NULL, data = df)fit = lm(depression ~ social_media_usage + NULL + physical_activity + factor(has_siblings),
data = df)fit = lm(depression ~ social_media_usage + age + NULL + NULL, data = df)fit = lm(depression ~ social_media_usage + age + NULL + factor(has_siblings), data = df)fit = lm(depression ~ social_media_usage + age + physical_activity + NULL, data = df)fit = lm(depression ~ social_media_usage + age + physical_activity + factor(has_siblings),
data = df)fit = lm(
depression ~ social_media_usage +
branch(age_covariate, "not_included" ~ NULL, "included" ~ age) +
branch(activity_covariate, "not_included" ~ NULL, "included" ~ physical_activity) +
branch(sibling_covariate, "not_included" ~ NULL, "included" ~ factor(has_siblings)),
data = df
)Results
Below, we show the estimated coefficients of our regresion model
model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 2 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 2.93 0.0699 41.9 7.37e-127 2.79 3.06
2 social_media_usage 0.197 0.0700 2.82 5.20e- 3 0.0593 0.335model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 3.22 0.131 24.6 1.62e-73 2.96 3.48
2 social_media_usage 0.185 0.0695 2.66 8.13e- 3 0.0484 0.322
3 factor(has_siblings)1 -0.406 0.154 -2.62 9.11e- 3 -0.710 -0.101model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.62 0.115 40.2 3.24e-122 4.39 4.84
2 social_media_usage -0.0327 0.0527 -0.621 5.35e- 1 -0.136 0.0710
3 physical_activity -0.459 0.0280 -16.4 1.67e- 43 -0.514 -0.404 model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.h…¹
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.58 0.128 35.7 2.75e-109 4.33 4.83
2 social_media_usage -0.0329 0.0527 -0.624 5.33e- 1 -0.137 0.0709
3 physical_activity -0.464 0.0290 -16.0 5.61e- 42 -0.521 -0.407
4 factor(has_siblings)1 0.0773 0.117 0.659 5.10e- 1 -0.153 0.308
# … with abbreviated variable name ¹conf.highmodel.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.66 0.927 5.03 0.000000867 2.83 6.48
2 social_media_usage 0.223 0.0711 3.14 0.00185 0.0835 0.363
3 age -0.127 0.0676 -1.87 0.0619 -0.260 0.00634model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 4 × 7
term estimate std.error statistic p.value conf.…¹ conf.…²
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.84 0.921 5.26 0.000000283 3.03 6.65
2 social_media_usage 0.210 0.0707 2.98 0.00316 0.0713 0.349
3 age -0.119 0.0670 -1.78 0.0761 -0.251 0.0126
4 factor(has_siblings)1 -0.394 0.154 -2.56 0.0111 -0.697 -0.0905
# … with abbreviated variable names ¹conf.low, ²conf.highmodel.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 5.76 0.677 8.51 8.56e-16 4.43 7.09
2 social_media_usage -0.0139 0.0537 -0.259 7.96e- 1 -0.119 0.0917
3 age -0.0844 0.0491 -1.72 8.69e- 2 -0.181 0.0123
4 physical_activity -0.456 0.0279 -16.3 3.04e-43 -0.511 -0.401 model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 5 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 5.73 0.678 8.46 1.29e-15 4.40 7.07
2 social_media_usage -0.0139 0.0537 -0.258 7.97e- 1 -0.120 0.0918
3 age -0.0855 0.0492 -1.74 8.34e- 2 -0.182 0.0114
4 physical_activity -0.462 0.0289 -16.0 8.23e-42 -0.518 -0.405
5 factor(has_siblings)1 0.0835 0.117 0.714 4.76e- 1 -0.147 0.314 model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 2 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 2.93 0.0704 41.6 3.97e-126 2.79 3.07
2 social_media_usage 0.139 0.0705 1.98 4.91e- 2 0.000542 0.278model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 3.25 0.131 24.7 5.63e-74 2.99 3.51
2 social_media_usage 0.146 0.0697 2.10 3.65e- 2 0.00928 0.284
3 factor(has_siblings)1 -0.444 0.155 -2.87 4.44e- 3 -0.749 -0.139model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.60 0.113 40.8 8.75e-124 4.38 4.82
2 social_media_usage 0.000872 0.0515 0.0169 9.86e- 1 -0.100 0.102
3 physical_activity -0.454 0.0273 -16.6 2.82e- 44 -0.508 -0.400model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.h…¹
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.56 0.126 36.2 8.70e-111 4.31 4.81
2 social_media_usage -0.00194 0.0517 -0.0375 9.70e- 1 -0.104 0.0999
3 physical_activity -0.459 0.0285 -16.1 1.83e- 42 -0.515 -0.403
4 factor(has_siblings)1 0.0773 0.118 0.657 5.12e- 1 -0.154 0.309
# … with abbreviated variable name ¹conf.highmodel.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.42 0.928 4.76 0.00000308 2.59 6.24
2 social_media_usage 0.158 0.0712 2.21 0.0275 0.0176 0.298
3 age -0.109 0.0677 -1.61 0.109 -0.242 0.0243model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 4 × 7
term estimate std.error statistic p.value conf.…¹ conf.…²
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.67 0.922 5.07 0.000000717 2.86 6.48
2 social_media_usage 0.164 0.0704 2.33 0.0205 0.0255 0.303
3 age -0.104 0.0669 -1.56 0.120 -0.236 0.0273
4 factor(has_siblings)1 -0.439 0.155 -2.84 0.00488 -0.743 -0.134
# … with abbreviated variable names ¹conf.low, ²conf.highmodel.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 5.82 0.674 8.63 3.76e-16 4.49 7.14
2 social_media_usage 0.0165 0.0520 0.316 7.52e- 1 -0.0859 0.119
3 age -0.0895 0.0488 -1.84 6.73e- 2 -0.185 0.00643
4 physical_activity -0.453 0.0272 -16.6 2.65e-44 -0.507 -0.399 model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 5 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 5.79 0.676 8.56 6.23e-16 4.46 7.12
2 social_media_usage 0.0136 0.0522 0.261 7.95e- 1 -0.0891 0.116
3 age -0.0901 0.0488 -1.85 6.58e- 2 -0.186 0.00593
4 physical_activity -0.458 0.0283 -16.2 1.54e-42 -0.514 -0.403
5 factor(has_siblings)1 0.0811 0.117 0.692 4.89e- 1 -0.150 0.312 model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 2 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 2.93 0.0708 41.4 1.69e-125 2.79 3.07
2 social_media_usage -0.0491 0.0709 -0.693 4.89e- 1 -0.189 0.0904model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 3.24 0.132 24.5 2.65e-73 2.98 3.50
2 social_media_usage -0.0594 0.0702 -0.846 3.98e- 1 -0.197 0.0787
3 factor(has_siblings)1 -0.439 0.156 -2.82 5.18e- 3 -0.746 -0.132 model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.60 0.111 41.4 2.63e-125 4.38 4.82
2 social_media_usage -0.0663 0.0507 -1.31 1.92e- 1 -0.166 0.0335
3 physical_activity -0.455 0.0269 -16.9 2.20e- 45 -0.508 -0.402 model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.h…¹
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.57 0.125 36.5 1.41e-111 4.32 4.81
2 social_media_usage -0.0648 0.0508 -1.28 2.03e- 1 -0.165 0.0352
3 physical_activity -0.459 0.0279 -16.4 1.29e- 43 -0.514 -0.404
4 factor(has_siblings)1 0.0697 0.117 0.595 5.52e- 1 -0.161 0.300
# … with abbreviated variable name ¹conf.highmodel.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.08 0.923 4.42 0.0000141 2.26 5.89
2 social_media_usage -0.0480 0.0708 -0.678 0.498 -0.187 0.0914
3 age -0.0841 0.0673 -1.25 0.212 -0.216 0.0483model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.…¹
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.31 0.917 4.71 0.00000390 2.51 6.12
2 social_media_usage -0.0582 0.0701 -0.830 0.407 -0.196 0.0798
3 age -0.0786 0.0666 -1.18 0.239 -0.210 0.0524
4 factor(has_siblings)1 -0.434 0.156 -2.78 0.00575 -0.741 -0.127
# … with abbreviated variable name ¹conf.highmodel.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 5.78 0.666 8.68 2.66e-16 4.47 7.09
2 social_media_usage -0.0651 0.0505 -1.29 1.98e- 1 -0.165 0.0343
3 age -0.0862 0.0480 -1.80 7.33e- 2 -0.181 0.00818
4 physical_activity -0.455 0.0268 -17.0 1.34e-45 -0.508 -0.402 model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 5 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 5.76 0.667 8.63 4.00e-16 4.44 7.07
2 social_media_usage -0.0635 0.0506 -1.26 2.10e- 1 -0.163 0.0361
3 age -0.0872 0.0480 -1.82 7.04e- 2 -0.182 0.00732
4 physical_activity -0.460 0.0278 -16.5 6.93e-44 -0.515 -0.405
5 factor(has_siblings)1 0.0764 0.117 0.655 5.13e- 1 -0.153 0.306 model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 2 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 2.93 0.0707 41.4 1.42e-125 2.79 3.07
2 social_media_usage -0.0669 0.0708 -0.945 3.46e- 1 -0.206 0.0725model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 3.23 0.133 24.4 6.81e-73 2.97 3.49
2 social_media_usage -0.0527 0.0703 -0.750 4.54e- 1 -0.191 0.0856
3 factor(has_siblings)1 -0.424 0.156 -2.71 7.08e- 3 -0.731 -0.116 model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.61 0.111 41.5 9.31e-126 4.39 4.82
2 social_media_usage -0.0986 0.0505 -1.95 5.20e- 2 -0.198 0.000840
3 physical_activity -0.456 0.0268 -17.0 9.50e- 46 -0.509 -0.403 model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.h…¹
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.56 0.125 36.6 7.12e-112 4.31 4.80
2 social_media_usage -0.102 0.0508 -2.02 4.47e- 2 -0.202 -0.00242
3 physical_activity -0.462 0.0279 -16.6 3.57e- 44 -0.517 -0.408
4 factor(has_siblings)1 0.0975 0.117 0.834 4.05e- 1 -0.133 0.328
# … with abbreviated variable name ¹conf.highmodel.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.02 0.926 4.34 0.0000198 2.19 5.84
2 social_media_usage -0.0596 0.0711 -0.839 0.402 -0.199 0.0802
3 age -0.0797 0.0675 -1.18 0.239 -0.213 0.0531model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.…¹
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.26 0.921 4.63 0.00000553 2.45 6.08
2 social_media_usage -0.0459 0.0705 -0.652 0.515 -0.185 0.0928
3 age -0.0756 0.0668 -1.13 0.259 -0.207 0.0559
4 factor(has_siblings)1 -0.420 0.156 -2.69 0.00762 -0.727 -0.112
# … with abbreviated variable name ¹conf.highmodel.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 5.69 0.666 8.54 6.96e-16 4.38 7.00
2 social_media_usage -0.0913 0.0506 -1.81 7.20e- 2 -0.191 0.00821
3 age -0.0795 0.0480 -1.65 9.91e- 2 -0.174 0.0151
4 physical_activity -0.456 0.0267 -17.1 6.83e-46 -0.509 -0.403 model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 5 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 5.66 0.668 8.47 1.17e-15 4.34 6.97
2 social_media_usage -0.0951 0.0508 -1.87 6.21e- 2 -0.195 0.00483
3 age -0.0805 0.0481 -1.67 9.52e- 2 -0.175 0.0141
4 physical_activity -0.463 0.0278 -16.7 2.32e-44 -0.517 -0.408
5 factor(has_siblings)1 0.102 0.117 0.876 3.82e- 1 -0.127 0.332 model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 2 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 2.93 0.0698 42.0 4.55e-127 2.79 3.06
2 social_media_usage 0.132 0.0438 3.01 2.80e- 3 0.0458 0.218model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 3.23 0.130 24.8 2.88e-74 2.98 3.49
2 social_media_usage 0.130 0.0433 3.00 2.97e- 3 0.0445 0.215
3 factor(has_siblings)1 -0.424 0.154 -2.76 6.17e- 3 -0.726 -0.121model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.61 0.115 40.1 5.73e-122 4.38 4.83
2 social_media_usage -0.0125 0.0330 -0.378 7.05e- 1 -0.0775 0.0525
3 physical_activity -0.457 0.0280 -16.3 3.32e- 43 -0.512 -0.402 model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.h…¹
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.57 0.128 35.8 1.56e-109 4.32 4.82
2 social_media_usage -0.0138 0.0331 -0.416 6.78e- 1 -0.0789 0.0514
3 physical_activity -0.462 0.0291 -15.9 1.56e- 41 -0.520 -0.405
4 factor(has_siblings)1 0.0797 0.117 0.678 4.98e- 1 -0.152 0.311
# … with abbreviated variable name ¹conf.highmodel.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.80 0.930 5.16 0.000000460 2.97 6.63
2 social_media_usage 0.152 0.0447 3.41 0.000751 0.0642 0.240
3 age -0.137 0.0678 -2.01 0.0448 -0.270 -0.00318model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 4 × 7
term estimate std.error statistic p.value conf.…¹ conf.h…²
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 5.01 0.924 5.42 1.22e-7 3.19 6.83
2 social_media_usage 0.149 0.0442 3.37 8.50e-4 0.0620 0.236
3 age -0.130 0.0671 -1.94 5.30e-2 -0.262 0.00170
4 factor(has_siblings)1 -0.414 0.153 -2.70 7.26e-3 -0.715 -0.113
# … with abbreviated variable names ¹conf.low, ²conf.highmodel.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 5.79 0.680 8.52 8.25e-16 4.46 7.13
2 social_media_usage 0.00144 0.0338 0.0425 9.66e- 1 -0.0652 0.0680
3 age -0.0875 0.0495 -1.77 7.80e- 2 -0.185 0.00986
4 physical_activity -0.454 0.0279 -16.2 7.23e-43 -0.509 -0.399 model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 5 × 7
term estimate std.error statistic p.value conf.low conf.h…¹
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 5.76 0.682 8.45 1.35e-15 4.42 7.11
2 social_media_usage 0.000214 0.0339 0.00631 9.95e- 1 -0.0665 0.0670
3 age -0.0881 0.0495 -1.78 7.61e- 2 -0.186 0.00931
4 physical_activity -0.460 0.0290 -15.8 2.85e-41 -0.517 -0.402
5 factor(has_siblings)1 0.0835 0.117 0.713 4.76e- 1 -0.147 0.314
# … with abbreviated variable name ¹conf.highmodel.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 2 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 2.93 0.0706 41.5 9.11e-126 2.79 3.07
2 social_media_usage 0.0440 0.0316 1.39 1.65e- 1 -0.0182 0.106model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 3.24 0.132 24.5 2.02e-73 2.98 3.50
2 social_media_usage 0.0437 0.0313 1.40 1.63e- 1 -0.0178 0.105
3 factor(has_siblings)1 -0.432 0.155 -2.78 5.83e- 3 -0.738 -0.126model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.64 0.113 41.0 2.48e-124 4.41 4.86
2 social_media_usage -0.0407 0.0232 -1.76 7.96e- 2 -0.0863 0.00484
3 physical_activity -0.465 0.0275 -16.9 2.28e- 45 -0.519 -0.411 model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.h…¹
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.59 0.126 36.4 2.12e-111 4.35 4.84
2 social_media_usage -0.0417 0.0232 -1.80 7.31e- 2 -0.0874 0.00393
3 physical_activity -0.471 0.0286 -16.5 1.08e- 43 -0.527 -0.414
4 factor(has_siblings)1 0.0890 0.117 0.762 4.47e- 1 -0.141 0.319
# … with abbreviated variable name ¹conf.highmodel.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.41 0.939 4.69 0.00000408 2.56 6.26
2 social_media_usage 0.0545 0.0322 1.69 0.0919 -0.00892 0.118
3 age -0.108 0.0685 -1.58 0.114 -0.243 0.0264model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.…¹
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.64 0.933 4.97 0.00000113 2.80 6.47
2 social_media_usage 0.0536 0.0319 1.68 0.0933 -0.00906 0.116
3 age -0.103 0.0678 -1.52 0.131 -0.236 0.0306
4 factor(has_siblings)1 -0.425 0.155 -2.74 0.00659 -0.730 -0.119
# … with abbreviated variable name ¹conf.highmodel.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 5.62 0.676 8.32 3.31e-15 4.29 6.95
2 social_media_usage -0.0334 0.0236 -1.41 1.58e- 1 -0.0799 0.0131
3 age -0.0725 0.0490 -1.48 1.40e- 1 -0.169 0.0240
4 physical_activity -0.463 0.0275 -16.9 3.73e-45 -0.517 -0.409 model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 5 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 5.59 0.678 8.25 5.44e-15 4.25 6.92
2 social_media_usage -0.0344 0.0237 -1.45 1.47e- 1 -0.0810 0.0122
3 age -0.0732 0.0491 -1.49 1.37e- 1 -0.170 0.0233
4 physical_activity -0.469 0.0285 -16.4 1.54e-43 -0.525 -0.413
5 factor(has_siblings)1 0.0924 0.117 0.792 4.29e- 1 -0.137 0.322 model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 2 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 2.97 0.0732 40.5 2.79e-115 2.82 3.11
2 social_media_usage 0.275 0.0734 3.75 2.18e- 4 0.131 0.420model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 3.21 0.137 23.4 2.46e-66 2.94 3.48
2 social_media_usage 0.266 0.0730 3.64 3.30e- 4 0.122 0.409
3 factor(has_siblings)1 -0.346 0.162 -2.13 3.37e- 2 -0.665 -0.0268model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.68 0.124 37.7 4.10e-108 4.44 4.93
2 social_media_usage 0.0118 0.0562 0.209 8.34e- 1 -0.0988 0.122
3 physical_activity -0.471 0.0308 -15.3 2.85e- 38 -0.532 -0.411model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.64 0.138 33.5 8.99e-97 4.37 4.91
2 social_media_usage 0.0113 0.0562 0.200 8.41e- 1 -0.0994 0.122
3 physical_activity -0.476 0.0317 -15.0 3.05e-37 -0.539 -0.414
4 factor(has_siblings)1 0.0794 0.122 0.649 5.17e- 1 -0.161 0.320model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.83 0.975 4.96 0.00000129 2.91 6.75
2 social_media_usage 0.302 0.0743 4.06 0.0000645 0.155 0.448
3 age -0.136 0.0711 -1.92 0.0560 -0.276 0.00352model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 4 × 7
term estimate std.error statistic p.value conf.…¹ conf.h…²
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 5.05 0.974 5.19 4.26e-7 3.14 6.97
2 social_media_usage 0.292 0.0740 3.95 1.01e-4 0.146 0.438
3 age -0.135 0.0706 -1.91 5.76e-2 -0.274 0.00438
4 factor(has_siblings)1 -0.342 0.161 -2.12 3.48e-2 -0.659 -0.0247
# … with abbreviated variable names ¹conf.low, ²conf.highmodel.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 6.45 0.714 9.02 4.03e-17 5.04 7.85
2 social_media_usage 0.0374 0.0565 0.662 5.09e- 1 -0.0739 0.149
3 age -0.129 0.0515 -2.51 1.28e- 2 -0.231 -0.0277
4 physical_activity -0.471 0.0305 -15.4 1.08e-38 -0.531 -0.411 model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 5 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 6.41 0.717 8.94 7.40e-17 5.00 7.82
2 social_media_usage 0.0370 0.0566 0.654 5.14e- 1 -0.0745 0.148
3 age -0.130 0.0516 -2.51 1.26e- 2 -0.231 -0.0280
4 physical_activity -0.476 0.0314 -15.2 1.11e-37 -0.537 -0.414
5 factor(has_siblings)1 0.0824 0.121 0.681 4.97e- 1 -0.156 0.321 model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 2 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 2.97 0.0741 40.0 4.12e-114 2.82 3.11
2 social_media_usage 0.204 0.0742 2.75 6.42e- 3 0.0578 0.350model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 3.25 0.138 23.5 1.45e-66 2.98 3.52
2 social_media_usage 0.210 0.0736 2.85 4.68e- 3 0.0650 0.355
3 factor(has_siblings)1 -0.397 0.163 -2.43 1.57e- 2 -0.718 -0.0754model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.69 0.122 38.4 8.77e-110 4.45 4.93
2 social_media_usage -0.00744 0.0551 -0.135 8.93e- 1 -0.116 0.101
3 physical_activity -0.474 0.0302 -15.7 1.27e- 39 -0.534 -0.415model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.65 0.136 34.2 1.14e-98 4.39 4.92
2 social_media_usage -0.0110 0.0554 -0.199 8.42e- 1 -0.120 0.0981
3 physical_activity -0.480 0.0313 -15.3 2.62e-38 -0.541 -0.418
4 factor(has_siblings)1 0.0821 0.123 0.668 5.05e- 1 -0.160 0.324 model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.66 0.988 4.72 0.00000391 2.71 6.61
2 social_media_usage 0.229 0.0753 3.03 0.00265 0.0803 0.377
3 age -0.124 0.0720 -1.72 0.0867 -0.266 0.0180model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 4 × 7
term estimate std.error statistic p.value conf.…¹ conf.…²
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.96 0.987 5.03 0.000000917 3.02 6.91
2 social_media_usage 0.235 0.0747 3.14 0.00186 0.0878 0.382
3 age -0.125 0.0714 -1.75 0.0811 -0.266 0.0156
4 factor(has_siblings)1 -0.399 0.163 -2.45 0.0149 -0.719 -0.0784
# … with abbreviated variable names ¹conf.low, ²conf.highmodel.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 6.42 0.716 8.96 6.27e-17 5.01 7.83
2 social_media_usage 0.0175 0.0556 0.315 7.53e- 1 -0.0919 0.127
3 age -0.126 0.0516 -2.44 1.53e- 2 -0.228 -0.0244
4 physical_activity -0.475 0.0299 -15.8 3.98e-40 -0.534 -0.416 model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 5 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 6.38 0.720 8.86 1.28e-16 4.96 7.79
2 social_media_usage 0.0139 0.0559 0.249 8.03e- 1 -0.0961 0.124
3 age -0.126 0.0516 -2.44 1.55e- 2 -0.227 -0.0241
4 physical_activity -0.480 0.0310 -15.5 8.74e-39 -0.541 -0.419
5 factor(has_siblings)1 0.0804 0.122 0.661 5.09e- 1 -0.159 0.320 model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 2 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 2.97 0.0750 39.6 5.66e-113 2.82 3.11
2 social_media_usage 0.0868 0.0751 1.16 2.49e- 1 -0.0611 0.235model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 3.23 0.141 22.9 1.51e-64 2.95 3.50
2 social_media_usage 0.0667 0.0752 0.887 3.76e- 1 -0.0813 0.215
3 factor(has_siblings)1 -0.363 0.167 -2.18 3.02e- 2 -0.691 -0.0349model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.70 0.120 39.1 1.24e-111 4.46 4.93
2 social_media_usage -0.0252 0.0539 -0.467 6.41e- 1 -0.131 0.0809
3 physical_activity -0.475 0.0295 -16.1 5.39e- 41 -0.533 -0.417 model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.66 0.136 34.3 6.89e-99 4.39 4.92
2 social_media_usage -0.0220 0.0542 -0.406 6.85e- 1 -0.129 0.0847
3 physical_activity -0.479 0.0304 -15.8 6.63e-40 -0.539 -0.420
4 factor(has_siblings)1 0.0749 0.123 0.610 5.42e- 1 -0.167 0.317 model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.28 0.994 4.31 0.0000235 2.32 6.24
2 social_media_usage 0.101 0.0758 1.33 0.184 -0.0482 0.250
3 age -0.0961 0.0724 -1.33 0.186 -0.239 0.0466model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.…¹
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.50 0.992 4.53 0.00000878 2.55 6.45
2 social_media_usage 0.0807 0.0758 1.06 0.288 -0.0686 0.230
3 age -0.0933 0.0720 -1.30 0.196 -0.235 0.0484
4 factor(has_siblings)1 -0.359 0.167 -2.16 0.0318 -0.687 -0.0314
# … with abbreviated variable name ¹conf.highmodel.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 6.37 0.714 8.93 7.67e-17 4.97 7.78
2 social_media_usage -0.00762 0.0539 -0.141 8.88e- 1 -0.114 0.0986
3 age -0.122 0.0512 -2.38 1.79e- 2 -0.223 -0.0212
4 physical_activity -0.477 0.0293 -16.3 1.09e-41 -0.535 -0.420 model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 5 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 6.34 0.716 8.86 1.25e-16 4.93 7.75
2 social_media_usage -0.00401 0.0542 -0.0740 9.41e- 1 -0.111 0.103
3 age -0.123 0.0513 -2.40 1.72e- 2 -0.224 -0.0220
4 physical_activity -0.482 0.0301 -16.0 1.24e-40 -0.541 -0.423
5 factor(has_siblings)1 0.0824 0.122 0.677 4.99e- 1 -0.157 0.322 model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 2 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 2.97 0.0750 39.5 7.14e-113 2.82 3.11
2 social_media_usage 0.0669 0.0752 0.890 3.74e- 1 -0.0811 0.215model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 3.25 0.140 23.1 2.20e-65 2.97 3.52
2 social_media_usage 0.0763 0.0747 1.02 3.07e- 1 -0.0707 0.223
3 factor(has_siblings)1 -0.390 0.166 -2.36 1.91e- 2 -0.717 -0.0643model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.70 0.120 39.2 7.59e-112 4.47 4.94
2 social_media_usage -0.0518 0.0539 -0.962 3.37e- 1 -0.158 0.0542
3 physical_activity -0.477 0.0295 -16.2 2.89e- 41 -0.535 -0.419 model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.h…¹
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.66 0.134 34.7 7.30e-100 4.39 4.92
2 social_media_usage -0.0554 0.0541 -1.02 3.07e- 1 -0.162 0.0512
3 physical_activity -0.483 0.0305 -15.8 4.85e- 40 -0.543 -0.423
4 factor(has_siblings)1 0.0909 0.122 0.742 4.59e- 1 -0.150 0.332
# … with abbreviated variable name ¹conf.highmodel.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.15 0.987 4.20 0.0000361 2.21 6.09
2 social_media_usage 0.0724 0.0753 0.961 0.337 -0.0758 0.221
3 age -0.0865 0.0720 -1.20 0.230 -0.228 0.0552model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.h…¹
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.44 0.986 4.50 0.0000103 2.50 6.38
2 social_media_usage 0.0818 0.0747 1.10 0.274 -0.0653 0.229
3 age -0.0870 0.0713 -1.22 0.224 -0.228 0.0534
4 factor(has_siblings)1 -0.391 0.165 -2.36 0.0188 -0.717 -0.0652
# … with abbreviated variable name ¹conf.highmodel.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 6.36 0.708 8.99 5.20e-17 4.97 7.76
2 social_media_usage -0.0450 0.0535 -0.841 4.01e- 1 -0.150 0.0603
3 age -0.121 0.0507 -2.38 1.81e- 2 -0.221 -0.0208
4 physical_activity -0.480 0.0293 -16.4 5.05e-42 -0.538 -0.422 model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 5 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 6.32 0.711 8.89 1.00e-16 4.92 7.72
2 social_media_usage -0.0486 0.0537 -0.905 3.66e- 1 -0.154 0.0572
3 age -0.121 0.0508 -2.38 1.79e- 2 -0.221 -0.0210
4 physical_activity -0.486 0.0303 -16.0 8.50e-41 -0.546 -0.426
5 factor(has_siblings)1 0.0931 0.121 0.767 4.44e- 1 -0.146 0.332 model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 2 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 2.97 0.0732 40.5 2.25e-115 2.82 3.11
2 social_media_usage 0.164 0.0428 3.82 1.67e- 4 0.0793 0.248model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 3.23 0.137 23.7 4.63e-67 2.96 3.50
2 social_media_usage 0.162 0.0425 3.81 1.71e- 4 0.0784 0.246
3 factor(has_siblings)1 -0.372 0.161 -2.30 2.20e- 2 -0.689 -0.0539model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.69 0.125 37.6 8.49e-108 4.44 4.93
2 social_media_usage 0.00139 0.0330 0.0422 9.66e- 1 -0.0636 0.0664
3 physical_activity -0.473 0.0310 -15.3 3.74e- 38 -0.534 -0.412 model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 4 × 7
term estimate std.error statistic p.value conf.…¹ conf.…²
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.65 0.138 33.6 4.26e-97 4.38 4.92
2 social_media_usage -0.0000325 0.0331 -0.000982 9.99e- 1 -0.0652 0.0651
3 physical_activity -0.478 0.0320 -15.0 5.77e-37 -0.541 -0.415
4 factor(has_siblings)1 0.0797 0.123 0.651 5.16e- 1 -0.162 0.321
# … with abbreviated variable names ¹conf.low, ²conf.highmodel.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 5.00 0.980 5.10 0.000000642 3.07 6.93
2 social_media_usage 0.184 0.0436 4.21 0.0000355 0.0977 0.270
3 age -0.149 0.0714 -2.08 0.0384 -0.289 -0.00798model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 4 × 7
term estimate std.error statistic p.value conf.…¹ conf.h…²
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 5.26 0.978 5.37 1.70e-7 3.33 7.18
2 social_media_usage 0.182 0.0433 4.20 3.61e-5 0.0967 0.267
3 age -0.148 0.0708 -2.09 3.77e-2 -0.287 -0.00849
4 factor(has_siblings)1 -0.370 0.160 -2.31 2.17e-2 -0.686 -0.0547
# … with abbreviated variable names ¹conf.low, ²conf.highmodel.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 6.45 0.718 8.99 5.23e-17 5.04 7.87
2 social_media_usage 0.0195 0.0335 0.582 5.61e- 1 -0.0464 0.0853
3 age -0.130 0.0519 -2.50 1.32e- 2 -0.232 -0.0273
4 physical_activity -0.471 0.0307 -15.4 2.01e-38 -0.532 -0.411 model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 5 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 6.41 0.721 8.89 1.03e-16 4.99 7.83
2 social_media_usage 0.0180 0.0336 0.537 5.92e- 1 -0.0481 0.0841
3 age -0.129 0.0520 -2.49 1.34e- 2 -0.232 -0.0271
4 physical_activity -0.476 0.0317 -15.0 3.15e-37 -0.539 -0.414
5 factor(has_siblings)1 0.0791 0.121 0.652 5.15e- 1 -0.160 0.318 model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 2 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 2.97 0.0736 40.3 1.01e-114 2.82 3.11
2 social_media_usage 0.0942 0.0285 3.31 1.07e- 3 0.0381 0.150model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 3.23 0.138 23.4 2.69e-66 2.95 3.50
2 social_media_usage 0.0918 0.0283 3.25 1.31e- 3 0.0362 0.147
3 factor(has_siblings)1 -0.362 0.163 -2.22 2.69e- 2 -0.682 -0.0416model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.71 0.124 37.9 1.79e-108 4.46 4.95
2 social_media_usage -0.0119 0.0217 -0.547 5.85e- 1 -0.0546 0.0309
3 physical_activity -0.479 0.0309 -15.5 5.67e- 39 -0.540 -0.418 model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.67 0.138 33.8 1.98e-97 4.40 4.94
2 social_media_usage -0.0125 0.0218 -0.574 5.66e- 1 -0.0553 0.0303
3 physical_activity -0.484 0.0318 -15.2 7.27e-38 -0.547 -0.421
4 factor(has_siblings)1 0.0826 0.122 0.675 5.00e- 1 -0.158 0.323 model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.90 0.988 4.96 0.00000124 2.96 6.85
2 social_media_usage 0.107 0.0290 3.68 0.000282 0.0497 0.164
3 age -0.142 0.0720 -1.97 0.0503 -0.283 0.000196model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 4 × 7
term estimate std.error statistic p.value conf.…¹ conf.h…²
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 5.14 0.986 5.21 3.77e-7 3.20 7.08e+0
2 social_media_usage 0.104 0.0288 3.62 3.52e-4 0.0477 1.61e-1
3 age -0.140 0.0715 -1.96 5.09e-2 -0.281 5.53e-4
4 factor(has_siblings)1 -0.359 0.162 -2.22 2.73e-2 -0.677 -4.05e-2
# … with abbreviated variable names ¹conf.low, ²conf.highmodel.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 6.38 0.719 8.87 1.13e-16 4.97 7.80
2 social_media_usage -0.000458 0.0221 -0.0207 9.83e- 1 -0.0439 0.0430
3 age -0.123 0.0520 -2.36 1.89e- 2 -0.225 -0.0204
4 physical_activity -0.477 0.0306 -15.6 3.36e-39 -0.537 -0.417 model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 5 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 6.34 0.722 8.78 2.15e-16 4.92 7.76
2 social_media_usage -0.00106 0.0221 -0.0478 9.62e- 1 -0.0446 0.0425
3 age -0.123 0.0520 -2.36 1.89e- 2 -0.225 -0.0204
4 physical_activity -0.482 0.0316 -15.3 4.22e-38 -0.544 -0.420
5 factor(has_siblings)1 0.0835 0.121 0.689 4.91e- 1 -0.155 0.322 model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 2 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 2.96 0.0720 41.1 1.54e-119 2.82 3.10
2 social_media_usage 0.251 0.0722 3.48 5.85e- 4 0.109 0.393model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 3.22 0.135 23.8 8.94e-69 2.95 3.48
2 social_media_usage 0.240 0.0718 3.35 9.31e- 4 0.0989 0.381
3 factor(has_siblings)1 -0.363 0.159 -2.28 2.34e- 2 -0.677 -0.0495model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.68 0.123 38.2 7.33e-112 4.44 4.93
2 social_media_usage -0.0171 0.0554 -0.308 7.58e- 1 -0.126 0.0920
3 physical_activity -0.476 0.0305 -15.6 1.20e- 39 -0.536 -0.416 model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.h…¹
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.64 0.137 34.0 2.33e-100 4.38 4.91
2 social_media_usage -0.0175 0.0555 -0.316 7.52e- 1 -0.127 0.0917
3 physical_activity -0.481 0.0315 -15.3 1.72e- 38 -0.543 -0.419
4 factor(has_siblings)1 0.0819 0.121 0.677 4.99e- 1 -0.156 0.320
# … with abbreviated variable name ¹conf.highmodel.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.50 0.948 4.74 0.00000342 2.63 6.36
2 social_media_usage 0.273 0.0732 3.73 0.000234 0.129 0.417
3 age -0.113 0.0693 -1.63 0.105 -0.249 0.0236model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.…¹
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.72 0.946 4.98 0.00000111 2.85 6.58
2 social_media_usage 0.262 0.0728 3.59 0.000386 0.118 0.405
3 age -0.110 0.0688 -1.60 0.111 -0.245 0.0255
4 factor(has_siblings)1 -0.359 0.159 -2.26 0.0249 -0.672 -0.0456
# … with abbreviated variable name ¹conf.highmodel.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 6.16 0.698 8.83 1.29e-16 4.79 7.54
2 social_media_usage 0.00418 0.0559 0.0747 9.41e- 1 -0.106 0.114
3 age -0.108 0.0504 -2.15 3.23e- 2 -0.208 -0.00921
4 physical_activity -0.475 0.0303 -15.7 6.15e-40 -0.535 -0.416 model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 5 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 6.13 0.700 8.75 2.22e-16 4.75 7.51
2 social_media_usage 0.00381 0.0560 0.0681 9.46e- 1 -0.106 0.114
3 age -0.109 0.0505 -2.16 3.15e- 2 -0.208 -0.00974
4 physical_activity -0.481 0.0313 -15.4 8.14e-39 -0.542 -0.419
5 factor(has_siblings)1 0.0865 0.120 0.719 4.72e- 1 -0.150 0.323 model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 2 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 2.96 0.0723 40.9 4.09e-119 2.81 3.10
2 social_media_usage 0.226 0.0725 3.12 2.00e- 3 0.0834 0.369model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 3.26 0.135 24.1 1.08e-69 2.99 3.52
2 social_media_usage 0.232 0.0717 3.23 1.37e- 3 0.0908 0.373
3 factor(has_siblings)1 -0.415 0.159 -2.60 9.73e- 3 -0.729 -0.101model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.67 0.121 38.5 7.84e-113 4.44 4.91
2 social_media_usage 0.000624 0.0546 0.0114 9.91e- 1 -0.107 0.108
3 physical_activity -0.473 0.0301 -15.7 4.02e- 40 -0.532 -0.413model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.h…¹
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.64 0.134 34.5 8.47e-102 4.37 4.90
2 social_media_usage -0.00324 0.0550 -0.0590 9.53e- 1 -0.111 0.105
3 physical_activity -0.478 0.0313 -15.3 1.26e- 38 -0.540 -0.417
4 factor(has_siblings)1 0.0822 0.122 0.675 5.00e- 1 -0.157 0.322
# … with abbreviated variable name ¹conf.highmodel.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.48 0.955 4.69 0.00000428 2.60 6.36
2 social_media_usage 0.249 0.0737 3.38 0.000825 0.104 0.394
3 age -0.112 0.0697 -1.60 0.111 -0.249 0.0258model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 4 × 7
term estimate std.error statistic p.value conf.…¹ conf.…²
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.78 0.952 5.02 0.000000928 2.91 6.65
2 social_media_usage 0.255 0.0729 3.50 0.000548 0.112 0.399
3 age -0.112 0.0690 -1.62 0.107 -0.247 0.0243
4 factor(has_siblings)1 -0.415 0.159 -2.61 0.00951 -0.728 -0.102
# … with abbreviated variable names ¹conf.low, ²conf.highmodel.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 6.20 0.700 8.86 1.04e-16 4.82 7.58
2 social_media_usage 0.0238 0.0552 0.431 6.67e- 1 -0.0849 0.133
3 age -0.112 0.0505 -2.22 2.76e- 2 -0.211 -0.0125
4 physical_activity -0.473 0.0299 -15.8 1.73e-40 -0.531 -0.414 model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 5 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 6.16 0.703 8.77 2.02e-16 4.78 7.55
2 social_media_usage 0.0199 0.0556 0.358 7.20e- 1 -0.0895 0.129
3 age -0.112 0.0506 -2.21 2.77e- 2 -0.211 -0.0123
4 physical_activity -0.478 0.0310 -15.4 5.51e-39 -0.539 -0.417
5 factor(has_siblings)1 0.0821 0.121 0.680 4.97e- 1 -0.156 0.320 model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 2 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 2.96 0.0735 40.2 2.01e-117 2.81 3.10
2 social_media_usage 0.0504 0.0737 0.684 4.94e- 1 -0.0946 0.195model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 3.24 0.138 23.5 1.42e-67 2.97 3.51
2 social_media_usage 0.0338 0.0734 0.460 6.46e- 1 -0.111 0.178
3 factor(has_siblings)1 -0.392 0.163 -2.40 1.70e- 2 -0.712 -0.0707model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.68 0.118 39.6 1.36e-115 4.45 4.91
2 social_media_usage -0.0366 0.0529 -0.691 4.90e- 1 -0.141 0.0676
3 physical_activity -0.475 0.0292 -16.3 3.34e- 42 -0.532 -0.417 model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.h…¹
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.64 0.133 34.9 8.03e-103 4.38 4.91
2 social_media_usage -0.0342 0.0531 -0.644 5.20e- 1 -0.139 0.0704
3 physical_activity -0.479 0.0301 -15.9 6.71e- 41 -0.539 -0.420
4 factor(has_siblings)1 0.0758 0.121 0.625 5.32e- 1 -0.163 0.315
# … with abbreviated variable name ¹conf.highmodel.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 3.95 0.963 4.10 0.0000534 2.06 5.85
2 social_media_usage 0.0607 0.0743 0.817 0.414 -0.0856 0.207
3 age -0.0730 0.0703 -1.04 0.300 -0.212 0.0655model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.h…¹
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.19 0.960 4.36 0.0000181 2.30 6.08
2 social_media_usage 0.0438 0.0740 0.592 0.555 -0.102 0.190
3 age -0.0699 0.0698 -1.00 0.317 -0.207 0.0675
4 factor(has_siblings)1 -0.388 0.163 -2.38 0.0179 -0.709 -0.0675
# … with abbreviated variable name ¹conf.highmodel.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 6.12 0.697 8.79 1.71e-16 4.75 7.50
2 social_media_usage -0.0221 0.0530 -0.417 6.77e- 1 -0.127 0.0823
3 age -0.105 0.0500 -2.10 3.66e- 2 -0.204 -0.00659
4 physical_activity -0.477 0.0290 -16.5 8.94e-43 -0.534 -0.420 model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 5 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 6.10 0.699 8.72 2.68e-16 4.72 7.47
2 social_media_usage -0.0194 0.0532 -0.363 7.17e- 1 -0.124 0.0855
3 age -0.106 0.0501 -2.12 3.51e- 2 -0.205 -0.00746
4 physical_activity -0.482 0.0299 -16.1 1.65e-41 -0.541 -0.423
5 factor(has_siblings)1 0.0833 0.121 0.691 4.90e- 1 -0.154 0.321 model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 2 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 2.96 0.0735 40.2 1.85e-117 2.81 3.10
2 social_media_usage 0.0601 0.0736 0.816 4.15e- 1 -0.0849 0.205model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 3.26 0.138 23.6 4.12e-68 2.99 3.53
2 social_media_usage 0.0795 0.0733 1.08 2.79e- 1 -0.0648 0.224
3 factor(has_siblings)1 -0.417 0.163 -2.56 1.10e- 2 -0.737 -0.0963model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.68 0.118 39.6 1.63e-115 4.45 4.92
2 social_media_usage -0.0385 0.0530 -0.726 4.68e- 1 -0.143 0.0658
3 physical_activity -0.475 0.0292 -16.3 3.60e- 42 -0.533 -0.418 model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.h…¹
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.64 0.132 35.1 2.57e-103 4.38 4.90
2 social_media_usage -0.0442 0.0535 -0.826 4.09e- 1 -0.150 0.0612
3 physical_activity -0.481 0.0303 -15.9 9.48e- 41 -0.541 -0.422
4 factor(has_siblings)1 0.0953 0.122 0.781 4.36e- 1 -0.145 0.335
# … with abbreviated variable name ¹conf.highmodel.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 3.86 0.955 4.05 0.0000676 1.98 5.74
2 social_media_usage 0.0613 0.0737 0.833 0.406 -0.0837 0.206
3 age -0.0664 0.0697 -0.952 0.342 -0.204 0.0709model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.h…¹
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.15 0.952 4.36 0.0000183 2.28 6.03
2 social_media_usage 0.0807 0.0733 1.10 0.272 -0.0636 0.225
3 age -0.0656 0.0690 -0.951 0.343 -0.202 0.0703
4 factor(has_siblings)1 -0.416 0.163 -2.56 0.0111 -0.737 -0.0956
# … with abbreviated variable name ¹conf.highmodel.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 6.16 0.692 8.90 7.81e-17 4.80 7.52
2 social_media_usage -0.0371 0.0526 -0.704 4.82e- 1 -0.141 0.0666
3 age -0.107 0.0496 -2.17 3.12e- 2 -0.205 -0.00975
4 physical_activity -0.478 0.0290 -16.5 7.70e-43 -0.535 -0.421 model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 5 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 6.12 0.694 8.82 1.38e-16 4.76 7.49
2 social_media_usage -0.0431 0.0532 -0.810 4.18e- 1 -0.148 0.0616
3 age -0.108 0.0496 -2.18 3.02e- 2 -0.206 -0.0104
4 physical_activity -0.485 0.0301 -16.1 1.95e-41 -0.544 -0.426
5 factor(has_siblings)1 0.100 0.121 0.825 4.10e- 1 -0.139 0.339 model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 2 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 2.96 0.0716 41.3 4.24e-120 2.82 3.10
2 social_media_usage 0.165 0.0421 3.90 1.19e- 4 0.0816 0.248model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 3.23 0.134 24.1 7.45e-70 2.97 3.50
2 social_media_usage 0.163 0.0418 3.89 1.26e- 4 0.0803 0.245
3 factor(has_siblings)1 -0.386 0.158 -2.44 1.52e- 2 -0.697 -0.0751model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.68 0.124 37.8 5.49e-111 4.44 4.92
2 social_media_usage -0.00578 0.0329 -0.176 8.61e- 1 -0.0705 0.0589
3 physical_activity -0.475 0.0308 -15.4 5.53e- 39 -0.535 -0.414 model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.h…¹
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.64 0.137 34.0 2.65e-100 4.37 4.91
2 social_media_usage -0.00737 0.0330 -0.223 8.23e- 1 -0.0723 0.0576
3 physical_activity -0.480 0.0319 -15.0 1.15e- 37 -0.543 -0.417
4 factor(has_siblings)1 0.0833 0.121 0.687 4.93e- 1 -0.155 0.322
# … with abbreviated variable name ¹conf.highmodel.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.74 0.950 5.00 0.00000104 2.88 6.61
2 social_media_usage 0.183 0.0430 4.24 0.0000299 0.0980 0.267
3 age -0.131 0.0694 -1.89 0.0600 -0.268 0.00556model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 4 × 7
term estimate std.error statistic p.value conf.…¹ conf.h…²
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 5.00 0.947 5.28 2.68e-7 3.13 6.86
2 social_media_usage 0.180 0.0427 4.23 3.21e-5 0.0964 0.264
3 age -0.129 0.0688 -1.88 6.13e-2 -0.265 0.00616
4 factor(has_siblings)1 -0.383 0.157 -2.43 1.55e-2 -0.693 -0.0733
# … with abbreviated variable names ¹conf.low, ²conf.highmodel.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 6.19 0.702 8.82 1.36e-16 4.81 7.57
2 social_media_usage 0.0102 0.0335 0.305 7.60e- 1 -0.0556 0.0761
3 age -0.111 0.0508 -2.19 2.96e- 2 -0.211 -0.0111
4 physical_activity -0.473 0.0306 -15.4 4.12e-39 -0.533 -0.413 model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 5 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 6.15 0.705 8.73 2.53e-16 4.77 7.54
2 social_media_usage 0.00863 0.0336 0.257 7.97e- 1 -0.0574 0.0747
3 age -0.111 0.0509 -2.19 2.95e- 2 -0.212 -0.0111
4 physical_activity -0.479 0.0317 -15.1 8.29e-38 -0.541 -0.416
5 factor(has_siblings)1 0.0845 0.120 0.701 4.84e- 1 -0.153 0.322 model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 2 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 2.96 0.0722 40.9 2.91e-119 2.81 3.10
2 social_media_usage 0.0940 0.0289 3.25 1.30e- 3 0.0370 0.151model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 3.24 0.135 24.0 2.37e-69 2.97 3.51
2 social_media_usage 0.0933 0.0287 3.25 1.28e- 3 0.0369 0.150
3 factor(has_siblings)1 -0.393 0.159 -2.47 1.41e- 2 -0.707 -0.0798model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.70 0.123 38.2 4.18e-112 4.46 4.94
2 social_media_usage -0.0158 0.0222 -0.713 4.76e- 1 -0.0595 0.0279
3 physical_activity -0.480 0.0306 -15.7 4.63e- 40 -0.540 -0.419 model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.h…¹
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.66 0.136 34.3 3.21e-101 4.39 4.92
2 social_media_usage -0.0170 0.0223 -0.765 4.45e- 1 -0.0609 0.0268
3 physical_activity -0.486 0.0317 -15.3 9.99e- 39 -0.548 -0.423
4 factor(has_siblings)1 0.0884 0.121 0.729 4.67e- 1 -0.150 0.327
# … with abbreviated variable name ¹conf.highmodel.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.57 0.956 4.78 0.00000290 2.69 6.45
2 social_media_usage 0.105 0.0295 3.54 0.000462 0.0465 0.163
3 age -0.118 0.0699 -1.69 0.0922 -0.256 0.0195model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 4 × 7
term estimate std.error statistic p.value conf.…¹ conf.…²
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.83 0.954 5.07 0.000000750 2.95 6.71
2 social_media_usage 0.104 0.0293 3.55 0.000456 0.0462 0.161
3 age -0.117 0.0692 -1.68 0.0932 -0.253 0.0197
4 factor(has_siblings)1 -0.391 0.159 -2.46 0.0144 -0.704 -0.0786
# … with abbreviated variable names ¹conf.low, ²conf.highmodel.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 6.13 0.701 8.74 2.40e-16 4.75 7.51
2 social_media_usage -0.00615 0.0226 -0.273 7.85e- 1 -0.0506 0.0383
3 age -0.105 0.0507 -2.07 3.95e- 2 -0.205 -0.00507
4 physical_activity -0.479 0.0304 -15.7 3.13e-40 -0.538 -0.419 model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 5 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 6.09 0.704 8.65 4.53e-16 4.70 7.47
2 social_media_usage -0.00736 0.0226 -0.325 7.45e- 1 -0.0519 0.0372
3 age -0.105 0.0508 -2.07 3.94e- 2 -0.205 -0.00515
4 physical_activity -0.485 0.0315 -15.4 6.63e-39 -0.547 -0.423
5 factor(has_siblings)1 0.0894 0.121 0.742 4.59e- 1 -0.148 0.327 model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 2 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 2.96 0.0718 41.2 3.30e-120 2.82 3.10
2 social_media_usage 0.254 0.0719 3.53 4.83e- 4 0.112 0.396model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 3.22 0.135 23.8 5.79e-69 2.95 3.48
2 social_media_usage 0.244 0.0716 3.41 7.51e- 4 0.103 0.385
3 factor(has_siblings)1 -0.358 0.159 -2.25 2.51e- 2 -0.672 -0.0451model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.67 0.122 38.4 1.08e-112 4.43 4.91
2 social_media_usage -0.0183 0.0554 -0.331 7.41e- 1 -0.127 0.0907
3 physical_activity -0.473 0.0303 -15.6 9.46e- 40 -0.533 -0.414 model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.h…¹
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.64 0.136 34.1 9.26e-101 4.37 4.90
2 social_media_usage -0.0188 0.0554 -0.340 7.34e- 1 -0.128 0.0903
3 physical_activity -0.478 0.0313 -15.3 1.32e- 38 -0.539 -0.416
4 factor(has_siblings)1 0.0753 0.121 0.624 5.33e- 1 -0.162 0.313
# … with abbreviated variable name ¹conf.highmodel.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.50 0.947 4.74 0.00000335 2.63 6.36
2 social_media_usage 0.276 0.0730 3.78 0.000191 0.132 0.420
3 age -0.112 0.0692 -1.62 0.105 -0.249 0.0238model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.…¹
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.71 0.946 4.98 0.00000112 2.85 6.57
2 social_media_usage 0.265 0.0726 3.65 0.000309 0.122 0.408
3 age -0.110 0.0687 -1.59 0.112 -0.245 0.0257
4 factor(has_siblings)1 -0.354 0.159 -2.23 0.0267 -0.666 -0.0412
# … with abbreviated variable name ¹conf.highmodel.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 6.16 0.698 8.83 1.29e-16 4.78 7.53
2 social_media_usage 0.00301 0.0559 0.0539 9.57e- 1 -0.107 0.113
3 age -0.109 0.0504 -2.16 3.17e- 2 -0.208 -0.00960
4 physical_activity -0.473 0.0301 -15.7 4.75e-40 -0.532 -0.414 model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 5 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 6.13 0.700 8.75 2.20e-16 4.75 7.50
2 social_media_usage 0.00260 0.0560 0.0465 9.63e- 1 -0.108 0.113
3 age -0.109 0.0504 -2.17 3.10e- 2 -0.209 -0.0101
4 physical_activity -0.478 0.0311 -15.4 6.13e-39 -0.539 -0.417
5 factor(has_siblings)1 0.0800 0.120 0.668 5.05e- 1 -0.156 0.316 model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 2 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 2.96 0.0721 41.1 7.92e-120 2.82 3.10
2 social_media_usage 0.232 0.0722 3.22 1.44e- 3 0.0903 0.375model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 3.26 0.135 24.1 5.88e-70 2.99 3.52
2 social_media_usage 0.239 0.0715 3.35 9.36e- 4 0.0985 0.380
3 factor(has_siblings)1 -0.413 0.159 -2.59 1.00e- 2 -0.726 -0.0994model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.66 0.121 38.7 1.54e-113 4.43 4.90
2 social_media_usage -0.00443 0.0547 -0.0809 9.36e- 1 -0.112 0.103
3 physical_activity -0.471 0.0300 -15.7 3.60e- 40 -0.530 -0.412model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.h…¹
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.63 0.134 34.6 3.53e-102 4.36 4.89
2 social_media_usage -0.00831 0.0551 -0.151 8.80e- 1 -0.117 0.100
3 physical_activity -0.476 0.0311 -15.3 1.13e- 38 -0.537 -0.415
4 factor(has_siblings)1 0.0768 0.121 0.632 5.28e- 1 -0.162 0.316
# … with abbreviated variable name ¹conf.highmodel.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.49 0.953 4.71 0.00000399 2.61 6.36
2 social_media_usage 0.256 0.0734 3.48 0.000582 0.111 0.400
3 age -0.112 0.0696 -1.61 0.110 -0.249 0.0253model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 4 × 7
term estimate std.error statistic p.value conf.…¹ conf.…²
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.79 0.950 5.03 0.000000866 2.91 6.66
2 social_media_usage 0.263 0.0727 3.61 0.000365 0.119 0.406
3 age -0.112 0.0689 -1.62 0.106 -0.248 0.0238
4 factor(has_siblings)1 -0.413 0.159 -2.60 0.00978 -0.725 -0.100
# … with abbreviated variable names ¹conf.low, ²conf.highmodel.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 6.19 0.699 8.85 1.12e-16 4.81 7.56
2 social_media_usage 0.0187 0.0553 0.339 7.35e- 1 -0.0901 0.128
3 age -0.112 0.0505 -2.21 2.80e- 2 -0.211 -0.0121
4 physical_activity -0.471 0.0298 -15.8 1.58e-40 -0.529 -0.412 model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 5 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 6.15 0.703 8.75 2.15e-16 4.77 7.53
2 social_media_usage 0.0149 0.0557 0.267 7.90e- 1 -0.0948 0.125
3 age -0.111 0.0505 -2.21 2.82e- 2 -0.211 -0.0120
4 physical_activity -0.476 0.0309 -15.4 5.07e-39 -0.537 -0.415
5 factor(has_siblings)1 0.0766 0.121 0.635 5.26e- 1 -0.161 0.314 model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 2 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 2.96 0.0734 40.4 4.98e-118 2.82 3.10
2 social_media_usage 0.0524 0.0735 0.713 4.76e- 1 -0.0922 0.197model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 3.24 0.138 23.5 1.09e-67 2.97 3.51
2 social_media_usage 0.0363 0.0732 0.496 6.20e- 1 -0.108 0.180
3 factor(has_siblings)1 -0.385 0.163 -2.37 1.86e- 2 -0.706 -0.0648model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.67 0.117 39.9 1.55e-116 4.44 4.90
2 social_media_usage -0.0374 0.0528 -0.709 4.79e- 1 -0.141 0.0665
3 physical_activity -0.472 0.0289 -16.3 2.24e- 42 -0.529 -0.415 model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.h…¹
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.63 0.133 35.0 3.02e-103 4.37 4.90
2 social_media_usage -0.0353 0.0530 -0.666 5.06e- 1 -0.140 0.0690
3 physical_activity -0.476 0.0298 -16.0 4.28e- 41 -0.535 -0.418
4 factor(has_siblings)1 0.0691 0.121 0.572 5.68e- 1 -0.169 0.307
# … with abbreviated variable name ¹conf.highmodel.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 3.95 0.963 4.10 0.0000548 2.05 5.84
2 social_media_usage 0.0627 0.0742 0.845 0.399 -0.0833 0.209
3 age -0.0721 0.0703 -1.03 0.306 -0.211 0.0663model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.h…¹
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.18 0.960 4.35 0.0000191 2.29 6.07
2 social_media_usage 0.0463 0.0739 0.626 0.532 -0.0992 0.192
3 age -0.0689 0.0697 -0.988 0.324 -0.206 0.0684
4 factor(has_siblings)1 -0.382 0.163 -2.35 0.0196 -0.703 -0.0616
# … with abbreviated variable name ¹conf.highmodel.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 6.12 0.696 8.79 1.69e-16 4.75 7.49
2 social_media_usage -0.0229 0.0529 -0.433 6.66e- 1 -0.127 0.0813
3 age -0.105 0.0500 -2.11 3.58e- 2 -0.204 -0.00707
4 physical_activity -0.475 0.0288 -16.5 5.80e-43 -0.531 -0.418 model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 5 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 6.09 0.698 8.73 2.63e-16 4.72 7.47
2 social_media_usage -0.0204 0.0531 -0.384 7.01e- 1 -0.125 0.0842
3 age -0.106 0.0501 -2.13 3.44e- 2 -0.205 -0.00787
4 physical_activity -0.479 0.0297 -16.2 1.02e-41 -0.538 -0.421
5 factor(has_siblings)1 0.0767 0.120 0.639 5.24e- 1 -0.160 0.313 model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 2 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 2.96 0.0733 40.4 3.81e-118 2.82 3.10
2 social_media_usage 0.0782 0.0734 1.07 2.87e- 1 -0.0663 0.223model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 3.26 0.138 23.7 2.22e-68 2.99 3.53
2 social_media_usage 0.0988 0.0731 1.35 1.78e- 1 -0.0451 0.243
3 factor(has_siblings)1 -0.417 0.163 -2.56 1.09e- 2 -0.737 -0.0969model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.68 0.118 39.8 2.68e-116 4.45 4.91
2 social_media_usage -0.0502 0.0531 -0.946 3.45e- 1 -0.155 0.0543
3 physical_activity -0.474 0.0291 -16.3 2.52e- 42 -0.531 -0.417 model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.h…¹
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.63 0.132 35.2 7.98e-104 4.37 4.89
2 social_media_usage -0.0565 0.0537 -1.05 2.94e- 1 -0.162 0.0493
3 physical_activity -0.480 0.0302 -15.9 6.75e- 41 -0.540 -0.421
4 factor(has_siblings)1 0.0942 0.122 0.773 4.40e- 1 -0.146 0.334
# … with abbreviated variable name ¹conf.highmodel.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 3.86 0.953 4.05 0.0000667 1.98 5.74
2 social_media_usage 0.0799 0.0734 1.09 0.277 -0.0647 0.224
3 age -0.0659 0.0696 -0.947 0.345 -0.203 0.0711model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.h…¹
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.15 0.951 4.37 0.0000179 2.28 6.02
2 social_media_usage 0.100 0.0731 1.37 0.171 -0.0435 0.244
3 age -0.0652 0.0689 -0.946 0.345 -0.201 0.0705
4 factor(has_siblings)1 -0.416 0.163 -2.56 0.0110 -0.737 -0.0962
# … with abbreviated variable name ¹conf.highmodel.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 6.16 0.691 8.91 7.24e-17 4.80 7.52
2 social_media_usage -0.0483 0.0527 -0.917 3.60e- 1 -0.152 0.0555
3 age -0.108 0.0495 -2.17 3.07e- 2 -0.205 -0.0101
4 physical_activity -0.477 0.0289 -16.5 5.28e-43 -0.534 -0.420 model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 5 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 6.12 0.693 8.83 1.28e-16 4.75 7.48
2 social_media_usage -0.0549 0.0534 -1.03 3.04e- 1 -0.160 0.0501
3 age -0.108 0.0495 -2.19 2.97e- 2 -0.206 -0.0107
4 physical_activity -0.484 0.0300 -16.1 1.36e-41 -0.543 -0.425
5 factor(has_siblings)1 0.0990 0.121 0.818 4.14e- 1 -0.139 0.337 model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 2 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 2.96 0.0714 41.5 8.06e-121 2.82 3.10
2 social_media_usage 0.167 0.0420 3.99 8.46e- 5 0.0848 0.250model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 3.24 0.134 24.2 4.03e-70 2.97 3.50
2 social_media_usage 0.166 0.0416 3.99 8.56e- 5 0.0840 0.248
3 factor(has_siblings)1 -0.383 0.158 -2.43 1.58e- 2 -0.694 -0.0727model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.67 0.123 38.0 1.10e-111 4.43 4.91
2 social_media_usage -0.00806 0.0329 -0.245 8.06e- 1 -0.0728 0.0566
3 physical_activity -0.473 0.0307 -15.4 5.00e- 39 -0.533 -0.412 model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.h…¹
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.63 0.136 34.1 1.12e-100 4.37 4.90
2 social_media_usage -0.00965 0.0330 -0.293 7.70e- 1 -0.0746 0.0553
3 physical_activity -0.478 0.0318 -15.0 1.03e- 37 -0.540 -0.415
4 factor(has_siblings)1 0.0774 0.121 0.640 5.23e- 1 -0.161 0.316
# … with abbreviated variable name ¹conf.highmodel.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.75 0.948 5.01 0.000000964 2.89 6.62
2 social_media_usage 0.186 0.0428 4.33 0.0000208 0.101 0.270
3 age -0.131 0.0693 -1.89 0.0592 -0.268 0.00512model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 4 × 7
term estimate std.error statistic p.value conf.…¹ conf.h…²
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 5.00 0.946 5.29 2.50e-7 3.14 6.86
2 social_media_usage 0.184 0.0425 4.33 2.12e-5 0.100 0.267
3 age -0.129 0.0687 -1.89 6.04e-2 -0.265 0.00567
4 factor(has_siblings)1 -0.380 0.157 -2.42 1.61e-2 -0.689 -0.0710
# … with abbreviated variable names ¹conf.low, ²conf.highmodel.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 6.18 0.701 8.81 1.43e-16 4.80 7.56
2 social_media_usage 0.00793 0.0335 0.237 8.13e- 1 -0.0579 0.0738
3 age -0.111 0.0508 -2.18 2.99e- 2 -0.211 -0.0109
4 physical_activity -0.471 0.0305 -15.4 3.79e-39 -0.531 -0.411 model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 5 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 6.14 0.704 8.72 2.65e-16 4.76 7.53
2 social_media_usage 0.00634 0.0336 0.189 8.50e- 1 -0.0598 0.0724
3 age -0.111 0.0509 -2.18 2.98e- 2 -0.211 -0.0109
4 physical_activity -0.476 0.0316 -15.1 7.62e-38 -0.538 -0.414
5 factor(has_siblings)1 0.0784 0.120 0.653 5.14e- 1 -0.158 0.315 model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 2 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 2.96 0.0719 41.2 4.98e-120 2.82 3.10
2 social_media_usage 0.0966 0.0285 3.39 8.04e- 4 0.0405 0.153model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 3.24 0.135 24.0 1.15e-69 2.98 3.51
2 social_media_usage 0.0964 0.0283 3.41 7.46e- 4 0.0407 0.152
3 factor(has_siblings)1 -0.391 0.159 -2.46 1.45e- 2 -0.704 -0.0781model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.69 0.122 38.4 1.09e-112 4.45 4.93
2 social_media_usage -0.0188 0.0220 -0.853 3.94e- 1 -0.0622 0.0246
3 physical_activity -0.479 0.0305 -15.7 4.31e- 40 -0.539 -0.419 model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.h…¹
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.65 0.135 34.4 1.42e-101 4.39 4.92
2 social_media_usage -0.0201 0.0221 -0.908 3.65e- 1 -0.0637 0.0235
3 physical_activity -0.484 0.0316 -15.3 9.32e- 39 -0.547 -0.422
4 factor(has_siblings)1 0.0840 0.121 0.695 4.88e- 1 -0.154 0.322
# … with abbreviated variable name ¹conf.highmodel.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.59 0.954 4.81 0.00000254 2.71 6.46
2 social_media_usage 0.107 0.0291 3.69 0.000273 0.0500 0.165
3 age -0.119 0.0697 -1.71 0.0888 -0.256 0.0182model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 4 × 7
term estimate std.error statistic p.value conf.…¹ conf.…²
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.85 0.952 5.09 0.000000652 2.97 6.72
2 social_media_usage 0.107 0.0288 3.71 0.000253 0.0501 0.164
3 age -0.118 0.0691 -1.70 0.0894 -0.254 0.0182
4 factor(has_siblings)1 -0.389 0.158 -2.46 0.0147 -0.700 -0.0771
# … with abbreviated variable names ¹conf.low, ²conf.highmodel.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 6.11 0.700 8.72 2.63e-16 4.73 7.49
2 social_media_usage -0.00924 0.0224 -0.413 6.80e- 1 -0.0533 0.0349
3 age -0.104 0.0507 -2.05 4.12e- 2 -0.204 -0.00418
4 physical_activity -0.478 0.0303 -15.7 3.12e-40 -0.537 -0.418 model.results = broom::tidy(fit, conf.int = TRUE)
model.results# A tibble: 5 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 6.07 0.703 8.63 4.99e-16 4.69 7.45
2 social_media_usage -0.0105 0.0225 -0.469 6.40e- 1 -0.0548 0.0337
3 age -0.104 0.0507 -2.05 4.12e- 2 -0.204 -0.00419
4 physical_activity -0.483 0.0314 -15.4 6.63e-39 -0.545 -0.421
5 factor(has_siblings)1 0.0847 0.120 0.705 4.81e- 1 -0.152 0.321 model.results = broom::tidy(fit, conf.int = TRUE)
model.resultsThe estimated effect of social_media_usage on
depression is shown in the figure below, along with other
estimated coefficients. If the coefficient was negative, it would imply
that increased social media usage is inversely associated with
depression i.e. social media usage has a positive impact on mental well
being; on the other hand if it is positive, it would imply that social
media usage is positively associated with depression i.e. social media
usage has a negative impact on mental well being. Finally, if the
coefficient is very close to zero, it suggests that the the relationship
is weak.
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() +
geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) +
xlim(c(-2, 2)) +
ylab("Model coefficients") +
theme_minimal() +
theme(
axis.title.x = element_blank()
)From the variation in the result, as shown in Figure 1, the impact of social media usage on depression appears to vary based on which choices we make in the data analysis process. This is more evident from the specification curve plot (Figure 2) which shows the variation in the outcome, with only less than half the specifications suggesting a positive effect.
data.spec_curve = extract_variables(M, model.results) %>%
unnest(model.results) %>%
# filter(stringr::str_detect(term, "^I")) %>%
filter(term == "social_media_usage") %>%
select( .universe, !! names(parameters(M)), estimate, p.value, conf.low, conf.high ) %>%
arrange( estimate ) %>%
mutate(
.universe = 1:nrow(.),
effect = ifelse(p.value < 0.05, ifelse(estimate < 0, "negative", "positive"), "not significant")
)
p1 <- data.spec_curve %>%
gather( "parameter_name", "parameter_option", !! names(parameters(M)) ) %>%
mutate( parameter_name = factor(stringr::str_replace(parameter_name, "_", "\n")) ) %>%
ggplot() +
geom_point( aes(x = .universe, y = parameter_option, color = effect), size = 1 ) +
labs( x = "universe #", y = "option included in the analysis specification") +
facet_grid(parameter_name ~ ., space="free_y", scales="free_y", switch="y")+
scale_colour_manual(values=c("#FF684B", "#999999", "#6E52EB")) +
theme_minimal() +
theme(strip.placement = "outside",
strip.background = element_rect(fill=NA,colour=NA),
panel.spacing.x=unit(0.15,"cm"),
strip.text.y = element_text(angle = 180, face="bold", size=10),
panel.spacing = unit(0.25, "lines")
)
p2 <- data.spec_curve %>%
ggplot() +
ggdist::geom_pointinterval(aes(x = .universe, y = estimate, ymin = conf.low, ymax = conf.high, color = effect)) +
labs(x = "", y = "effect size") +
theme_minimal() +
scale_colour_manual(values=c("#FF684B", "#999999", "#6E52EB"))
cowplot::plot_grid(p2, p1, axis = "bltr", align = "v", ncol = 1, rel_heights = c(1, 3))## Warning: Using the `size` aesthietic with geom_segment was deprecated in ggplot2 3.4.0.
## ℹ Please use the `linewidth` aesthetic instead.
Conclusion
As a result, we conclude that the association between social media usage and depression is not robust to arbitrary choices in the data analysis process, and any impact that prior work has found on social media usage on depression is likely due to idiosyncratic choices in the data analysis process.